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In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modelling framework that utilizes the tools of ML methods. This framework offers readers a flexible modelling strategy since it accommodates cases from the simplest linear models to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses: what properties are desirable in an estimator; basic techniques for finding ML solutions; the general form of the covariance matrix for ML estimates; the sampling distribution of ML estimators; the application of ML in the normal distribution as well as in other useful distributions; and some helpful illustrations of likelihoods.

Maximum Likelihood Estimation 2024 pdf epub mobi 電子書 下載

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發表於2024-05-14

Maximum Likelihood Estimation 2024 pdf epub mobi 電子書 下載

Maximum Likelihood Estimation 2024 pdf epub mobi 電子書 下載

Maximum Likelihood Estimation 2024 pdf epub mobi 電子書 下載

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圖書標籤: 方法論  statistics   

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